Multilingual (or cross-lingual) embeddings represent several languages in a
unique vector space. Using a common embedding space enables for a shared
semantic between words from different languages. In this paper, we propose to
embed images and texts into a unique distributional vector space, enabling to
search images by using text queries expressing information needs related to the
(visual) content of images, as well as using image similarity. Our framework
forces the representation of an image to be similar to the representation of
the text that describes it. Moreover, by using multilingual embeddings we
ensure that words from two different languages have close descriptors and thus
are attached to similar images. We provide experimental evidence of the
efficiency of our approach by experimenting it on two datasets: Common Objects
in COntext (COCO) [19] and Multi30K [7].

本文提出一个使用多语言嵌入词汇表达图像语义信息的框架，将图像和文本嵌入到一个唯一的分布向量空间中，从而使得我们可以使用描述图像内容的文本查询来搜索图像，同时也可以使用图像相似性，我们使用实证研究证明了该方法的效率。